More data, more problems

Manufacturers are not short of data. If anything, they are submerged in it. Every machine, sensor, line and supplier now generates a constant stream of information. Production speeds, temperatures, rejects, downtime codes, quality checks, changeovers. The volume has accelerated dramatically over the past decade, yet understanding has not kept pace. Many plants that describe themselves as data-driven still struggle to answer basic operational questions in real time. Why did the line stop? What is driving waste on this product? Are we genuinely on track to hit today’s target?

 

Part of the problem is that more dashboards have not equalled more clarity. In many factories, data lives across legacy systems, spreadsheets and point solutions that were never designed to connect seamlessly. Information becomes fragmented. Context gets lost. Noise builds. It is entirely possible to have multiple screens in a control room and still rely on yesterday’s interpretation of events.

 

Most companies do not have truly live data. There is often manipulation involved before insight reaches the people who need it. Reports are exported, reconciled, circulated and discussed. By then, the value has already diminished. If I tell you the line stopped for 1.5 hours yesterday because of an electrical fault on the metal detector, that is informative. If I can tell the electrical engineer and the supervisor that the line stopped 2 minutes ago for that exact reason, that becomes operationally powerful. The difference is not marginal. It determines whether the issue is analysed or eliminated.

 

The deeper issue is that many organisations do not lack data, they lack actionable data. They have metrics on almost everything, but not focused insight on the losses that truly matter. Leaders can find themselves staring at trends and averages without a clear view of what to do next. One manufacturer recently told us they receive an alarm every 2 minutes whenever a line stops. At first glance that sounds proactive. In practice, it creates fatigue. When alerts are constant and undifferentiated, people start to tune them out. The signal disappears inside the noise.

 

This is where false confidence creeps in. Boards believe they are data-led because information is visible. Yet if the data is not connected, contextualised and aligned to specific operational outcomes, decisions can drift off course faster than you realise. Production planning slips into a reactive rhythm rather than a predictive one, with teams responding to yesterday’s output instead of anticipating today’s risks. Maintenance schedules are shaped by historic summaries and averages, not by live patterns of loss that point to where failure is building. And when it comes to investment, decisions lean on broad performance indicators instead of zeroing in on the precise pain points that are quietly eroding margin on the shop floor.

 

All of this unfolds against a backdrop of rising pressure on operators. Standards increase; quality checks multiply; lead times shrink; changeovers become more frequent; product portfolios grow more complex. Factories operate in an environment shaped by volatility and unpredictability, yet expectations continue to rise. Without the right supporting technology, the burden on frontline teams can become unrealistic.

 

Turning data into insight requires context and coherence. Manufacturing data only becomes useful when it is connected across systems, time-stamped accurately and aligned to outcomes such as throughput, quality, downtime and waste. It must answer practical questions in the moment. What loss is happening right now, why is it happening, who is best placed to resolve it, and what action will eliminate it quickly?

 

It becomes even more powerful when enriched with operator input. The human perspective remains critical to performance. Operators can provide nuance that no sensor can detect. They understand when a material change introduced friction, when a tool was slightly misaligned, or when a workaround was applied to keep output moving. Capturing those insights and embedding them within the data transforms numbers into meaningful intelligence. It also reinforces engagement. Factories are run by people, and performance improves when people are involved and feel part of the solution, rather than overwhelmed by systems.

 

Speed is equally decisive. Labour shortages persist, supply chains remain fragile and margins are tight, all while consumers expect ever shorter lead times. That pressure cascades from retailers to manufacturers, compressing timelines at every stage. Product life cycles are shrinking and new product development has to move quickly from concept to stable production without costly missteps. In that environment, retrospective analysis is simply not enough. Manufacturers that can identify and resolve losses in shift protect their competitiveness and their margins. Those that only uncover problems days later end up absorbing costs that could have been avoided.

 

The organisations gaining ground are not necessarily collecting more data. They are focusing on making their existing data usable. That means integration rather than proliferation, standardisation rather than fragmentation, and tools that surface what matters instead of everything that can be measured. It requires a shift from engineering-centric visibility to frontline-centric action.

 

We recently spent three hours with a prospect operating more than 75 factories globally. They had invested over $12m in an operations data system that delivered extensive reporting capability. Yet much of the output was difficult to translate into immediate action. Teams spent time navigating dashboards rather than eliminating losses. They quickly recognised that layering a manufacturing performance execution platform over their existing infrastructure would not duplicate capability but sharpen it. The value lay in turning visibility into intervention.

 

Traditional SCADA and historian platforms provide a strong automation backbone and reliable data capture. What they often lack is a mechanism to prioritise losses, surface root causes automatically and direct the right information to the right person at the right moment. A purpose-built performance platform can focus on overall equipment effectiveness, downtime, waste, make-ready and throughput, using AI to identify patterns and prompt action within the shift. Real-time alerts are differentiated and meaningful rather than constant and generic. Operator feedback loops are embedded. Workflows are digital and aligned to site priorities. Configuration reflects the realities of each factory without requiring prolonged custom projects.

 

The outcome is not additional complexity but clarity. With the right insight in front of them, teams can intervene earlier and reduce losses before they escalate into bigger problems. Firefighting begins to ease because issues are addressed in the moment rather than unravelled after the fact. Early improvements create momentum and credibility, making it easier to scale across sites. And at leadership level, there is renewed confidence that the numbers being reviewed reflect what is happening now, not a carefully assembled interpretation of yesterday.

 

Manufacturing is entering a phase where clarity will determine competitiveness. The advantage isn’t with those who have accumulated the largest volumes of data, but with those who can translate raw signals into timely, trusted insight that drives decisive action. More data on its own does not deliver progress. Insight, delivered with context and speed, is what sustains performance.

 

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Jonathan Newton is General Manager Europe, OF Systems

 

Main image courtesy of iStockPhoto.com and PeopleImages